{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "macro = pd.read_csv('./data/pydata-book/ch08/macrodata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = macro[['cpi','m1','tbilrate','unemp']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "trans_data = np.log(data).diff().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>0.021327</td>\n",
       "      <td>0.018115</td>\n",
       "      <td>0.109199</td>\n",
       "      <td>0.097164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>-0.007904</td>\n",
       "      <td>0.045361</td>\n",
       "      <td>-0.396881</td>\n",
       "      <td>0.105361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>-0.021979</td>\n",
       "      <td>0.066753</td>\n",
       "      <td>-2.277267</td>\n",
       "      <td>0.139762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>0.002340</td>\n",
       "      <td>0.010286</td>\n",
       "      <td>0.606136</td>\n",
       "      <td>0.160343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>0.008419</td>\n",
       "      <td>0.037461</td>\n",
       "      <td>-0.200671</td>\n",
       "      <td>0.127339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>0.008894</td>\n",
       "      <td>0.012202</td>\n",
       "      <td>-0.405465</td>\n",
       "      <td>0.042560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          cpi        m1  tbilrate     unemp\n",
       "197  0.021327  0.018115  0.109199  0.097164\n",
       "198 -0.007904  0.045361 -0.396881  0.105361\n",
       "199 -0.021979  0.066753 -2.277267  0.139762\n",
       "200  0.002340  0.010286  0.606136  0.160343\n",
       "201  0.008419  0.037461 -0.200671  0.127339\n",
       "202  0.008894  0.012202 -0.405465  0.042560"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans_data[-6:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x107d8e250>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.scatter(trans_data['m1'],trans_data['unemp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9zzn3fufcDcCv4yhTREprleTbZtXqrQuaM0eaRaI5JGZ2ENAPfCS/zTnnzGwcWNUsZYos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\n3IsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107c8d910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x107d43950>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x107e85590>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x107f073d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x107f5bed0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x107fe0d50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x108019250>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1080d1c10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x108154a90>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1081b8d50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10823abd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1082a48d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10832c9d0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1083af850>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10841e2d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1084a3150>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x108508190>]], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.scatter_matrix(trans_data,diagonal='kde',c='k',alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OHjyoJk2qr68nOTmZjIwMALKzs0lLS8PlcmGz2cjLy+PkyZOcOnWKoqIiCgsL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\nPERy5d+oEEKCVDvmO0DAGhAgB+CVV57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CM888wxVXXMHWrVt59tlngT/6M0TDXISZ16m4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\nGhgYGCx33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loaHo9nUdyj80ngOQ6WdRXsmprpHC+FabxwGB+H/fsVEbK5ok7J/N//wV//\ntf59Ww5E6gn5MXACuFxK6QYQQqSjaHT8D0Hk1aMlTNn2YZRpmmDbfx/4/lzb3bp1K7m5uTE/gEbz\nthv4sMnJydHt7dIoFz47ek2XhLoGIj0HsTQIBQ6yb731FgCFhYVceumly97IDXaOw3kezHSOl8I0\nXjgcPw59fXDttXPfNjMTrrtOiQsxjJDIiNQI2YSfAQJKeqwQ4kvAW7r0LEZYvXo1VVWxLy0S7G03\n3DfZwIcN6JudYaAQ6nzoNV2it9cplgahwEFWNUIMIzc44V4Loc6xlBKPx4PD4cDpdJKfn79knwOv\nvaZMw7zrXZFtf9dd8Dd/A93dilFiMDciNUIagVwUb4g/OcCpqHoUgmgVUyeXfxj4PEq8yKvAJ6SU\n4/PR34Um2MO4sbExrDfZwEGwsrJSC2hczm+XehPqrVMv40HvAXkpxJIsV8K9FkKd46amJurr67FY\nLFpc3FJ9Drz2Glx5JUzeknPm9tsVL8jzzytF7QzmRqRGyD8C3xFC/Avwx8nPrgC+DHxeCJGiriil\n7I+qhxeISjFVCFEEfAXYNBno+izwUWBJaoVA+G+ywQZB/7S95RSgNp+EOh8zDRgXMy7DiOdZuqjX\nldPppKKiApvNRnZ29pRz7vP52L59O/X19djt9iV5z6vxIJ/5TOT7WLECtm2DZ54xjJBIiNQIeWHy\n91MomTFwIRvleb//JRAXYRsafoqpN4KimCqE+J4QosQ/Q2ZyeujQZJ2YQO5GCWB1Tv7/KIoxFRNG\nyHwMNuG+yc4WFa9noOpyJhLPwkLEZYS69sJ5m46l4FWD6YQ6P42NjTz++OO43W7S09O5//77p5zn\n5eIFiyYexJ8774TPfx76+yElZfb1DS4QqRFyna69mB09FFMLgTN+/7dOfhYTzMdgE+mbrN6BqsZA\npRDJ+ZjJmzXfqb3zva3B/BPq/Bw9epSamhoyMjJob2/n6NGjVPjplYe6Vpfavfzaa4o+yLsDfepz\n5M474aGH4Pe/h3vv1aVry4aIjBAp5et6dyQCor3yY+rOmY8gwEhjBPQOVDUGKoVIzsdMb6Tzndo7\n39sazD+Rnp9Q1+pSu5dfew22bo08HkSlsBAuv1yZkjGMkLkRjViZFdiIEow6pRCelPK5KPsVSNSK\nqZPr+he8Wx3O9g899BCpqalTPpsP2XY93J96vaXoHai60ANVLMu2z5WZvCczZTbM5TqI5tpbLm77\nxUqw8yOlJC0tjfT0dHw+H+vXr6eysjKs/S0lo1OPeBB/7rwT/v3flUq8cxU9W85EKla2E/g5SqZK\nILrEgUzZoQ6KqcAzwIHJYFonivjZr2dr++GHH16QFF09ggD1ekuZLVB1riz0QBXLsu1zZSbvyUyZ\nDXO5DqK59ozg1dgm2PlpamqioaGBnJwcfD4fV199ddj39lIyOmtqlHiQuUi1z8Rdd8EXvwh79igi\nZgbhEakn5LvAb4CvSCk7dezPTESlmCqlbBFC/DNwCMVQ2oeScRMTzMVVH26NkEjqRwTry1yyY4L1\nzRio5gdVC+bo0aOAcuyllDidTtrb28nKyqK9vR2n00lpaSl79+6ltbWVoqIibrzxRkwmxYEZTWqv\nodMR2wQ7P8EyX0BJ6Xc6nQwMDGCz2TSvybFjxwCorKykrKwsaNXnxcj+/co0TLTxICrl5XDppYpw\nmWGEhE+kRkgu8K0FNECiVkydXP4T4Cfz0sEFxP9Nt6+vj/Xr11NVVUVmZmbI+hEWi4WWlhbsdjtZ\nWVmUlpZy6tSpsAyLubxZh1p3PgeqxRos558mqT74/dMkg32nYNVku7q68Hq9dHV1IYRgYGCA5uZm\nTpw4gdVqZWBggL179/I///M/jIyMYLVaAdixY8dF++4GF4/MzEz6+vrYvXs36enpZGZmatkyLS0t\nOJ1ONm3ahMViobu7WxMsS09P56677uJDH/rQkjA6X38dtmxRAlP14q674JFHwOcDi0W//S5lIjVC\nngauBZr164pBuKgeD7vdzsGDB3G73XR1dbFjx44pbyn+9SMOHDhAS0sL+fn5WCwWbDYbNTU1WCwW\n8vPzgdCGxVzmgRcimyOQxRosp/a7vb2d5uZm1qxZQ0FBAVJKWltb+f3vf8/g4CDJycncfPPN3HTT\nTWFlLtlsNtasWUNWVpb2f21tLSMjI1RVVVFdXU1ra+vF/voGMYSaLTM2NkZ7ezurVq3C7XYzMDCA\nlJKOjg4cDodWOHDdunWLyuAPRErFE/Lxj+u73zvvhK98BfbtA8PGD49IjZC/BX4jhNgO1ACj/gul\nlN+JtmMGoVHnZWtqagDYsGEDHo+H7u5utm7dGrR+hM/nw2KxaAZJa2srg4ODmgHicrm0+eJoZMUj\nzeaIxkAJZvgEfpeFYLbvELhcNRKzsrI4ceIEWVlZjIyM8Pvf/54333yTlpYW4uPjtYJtxcXFYWcu\nFRQU4PV6KSgoIDs7m6KiIqxWK9XV1VitVoqKihbkmBjEHt3d3aSmprJlyxbq6+vp7u7WliUkJNDX\n18fLL79MZmYmSUlJ9PT0MDw8THl5OUNDQ+zZs4eGhgZ8Ph+7du3ipptuWnSGSH09uFxw9dX67nfj\nRlizRsmSMYyQ8IjUCLkPuAkYQfGISL9lEtDdCAlHtn1yvaDS7JMCZi8B9VwQUrtSSunVu6/zjequ\nz8nJoba2lv7+fqxW67TB1j8WQ600qhokKSkppKWl4XA4SEpKIisrSxdZ8UiyOWCqgRI4dRRJdkfg\nd/H5fHM9zHNmNo9M4PKKigoSEhJob2/HarXicrmwWCy0tbXR1dXFyMgIg4ODFBcX43Q6qa6uprKy\nMuzMJf/PSktLAabEhBgsT4LdL5mZmdTW1nLs2DGklIyNjSGEID8/n+zsbBobG/F6vSQmJiKlpLe3\nF4fDAUBxcfGi8Dz6s38/xMUpcu16IoTiDXnsMfjv/1baMJiZSI2QrwL/DHwtQEBsPplVtj0Mafb6\nwHoyixE12KysrIyqqipcLheZmZlIKTl06NC0jBY1bVN9k/Z4PNTV1dHR0UFSUhK7du2irKyMN998\nM6SsuL9xAYQ0DCLJ5lD3G2zqKNLsjsDvorqR5xP1O1RUVHDw4EH27dun9S9Y4LDNZmPHjh1UV1eT\nl5fHihUrEEJoXquhoSHGxsYYGhrC4/FQW1tLZWXltMDAYJlLgedACGHEgBgAoV8UHnjgAXw+H11d\nXaSlpXH27FkGBgaorKwkOzsbh8NBYWEhnZ2d1NbWUlBQgMViWZRpuvv3w+bNYLPpv++77oJvfAMO\nHtQv82YpE6kRYgGeXCgDJFzZdmaXZl9cPsNZ8B/wGxsbeemll3A4HPh8Pnbu3ElxcTHd3d3ag0Zd\nd2JCOW2JiYnaW/Fs0y56xF3MVOn37Nmz9PX1TZs6CkeLIJjhE/hdFsJdrLZ58OBBmpuVcCmvV3G0\nlZeXT+tTdnY2AE6nk7GxMZxOJ+Xl5ZjNZoaGhli3bh0rV66kr6+PjRs30t/fH3TKLVJCTR+p2VBq\n1k1lZaVmjBosLkKd48DsN3WdiooKmpqacLvdrFixgk2bNjE6Okp6ejotLS14PB5tGrG7u5uysjIt\ni2axBIdLqQSl6iz1pPGud0F+vpIlYxghsxOpEfIz4APAf+jYl5kIV7Z9Nmn2EiHE28A48JiUMibq\nxuiBy+XC4XBoblK3282qVatITU2dYjRIKdm7dy8vvfQSFouFkZERzZ0a6VRKuMxU6XdkZASAVatW\nsXHjRm3qKFItgsDvopZ+n0/UNlUPyLZt22hoaNCOVTgeG7fbjRBCE5Patm0bjY2NeDyeoFNugcxl\nMAhlWDY1NfH4449rMUe1tbU88MADi+5t1yC8OKzq6mpqa2tJTU3FbDZz6623MjY2RlFRERMTE/z4\nxz/m3LlzeDweioqKSE1NZevWrQwNDbFhwwYt/mqxBIe3toLDoX88iIrJpEzJ/Pa38PDDyv8GoYnU\nCIkDPieE2AG8w/TA1E9H27EwCMfM9l/nCFAgpfQIIfKB3wshnFLKp2fawUIppkZLVlYWPp8Ph8NB\nfn4+g4ODuN1uLfhMHQibmpp46aWXaGpqIj8/HyklR44c4ciRI5w/f568vDxND8B/8JovkSLVuFm3\nbh319fUUFhZy5ZVXalNHkWgRXCzFVP9pEa/XS0NDw5RjFcpjY7FYOHDgAD6fj/j4eFJSUrTzZrPZ\nqKio0GI51NiOUMxlMAhlWDqdTlpaWhgbGyMhIYGenp5F6XI3IKhmTGAcVn19PQ0NDVRVVSGE4L3v\nfS85OTm4XC7OnDlDSUkJJSUlVFdXMzw8TGJiIkIIKioqtG0Wk5Lq/v1K7Ma2bfPXxp13wne/C2+/\nrZ8OyVIlUiNkA3B08u/1Acsk+hOubHtIaXYp5YDWQSkdQohfAdtR0o1DslCKqdFSVlbGrl27ALBY\nLGRnZyOEmCbXXF1djdPpJDk5mcbGRhITE2lvb6ezs5P29nbsdjvvfve7+dSnPhVWQatoCWbcRCuA\ntRCKqTNNZUxMTDA+Ps7w8DAbNmyY0XAoKyujpaWFlpYWbX7d/7wNDg7S0NCgGTWzBQHOZTAIZVgO\nDAzgdDpxOByYTCZyc3MXtTLmciaYZoyKeq3k5uZy4MAB3njjDfLy8mhoaODIkSM4HA46Ozvx+Xyk\npaVRXl7O1VdfTUZGhqZpU1paSmNj45Tp1FhXUt2/HzZsgPT0+Wtj+3bIzlayZAwjZGYiLWC3oFV0\n5yDbHlKaXQixAuiUUkohhB24FfjxQn2H+UYIwU033aR5EDIzMwGmxIQ0NTVRW1vLwMAAXV1djI+P\nY7fbaW9vx+v1Mjw8jMlkora2dlpVzflSxgzHuInF+eaZpjJ+8YtfaFMZPp+PkpKSkMdNCIHdbic/\nP5+1a9dSV1eH2WwmKSmJoqIikpOT5/SGORePVahjb7PZ2LRpE5dddhkdHR1cc801i1oZczkTTDNG\nRb1WWlpayMnJ0bwao6Oj2tRua2srw8PDlJWVUVxcTGVlJSaTSQtQb2pqYs+ePVOmU6uqqmL6etm/\nH3bunN824uLg9tuVKZmvfU3xvBgEJ+ICdqClza4B9ksph4UQQko5H54QCEO2fRZp9ruAvxZCjKJ8\n76eklI/NU18vCsEMBXUAf/PNNzl79ix2u52qqir27duHzWbjsssu4+c//zm9vb0ApKenY1lAqb9w\njJtYnG8O5XFwuVy43W4yMjIAcLvdMxoOUko8Ho+mSmk2mxFCMDY2RkNDAxUVFVOmazweD1LKaUaY\nGkxaXV3N+Pg4q1at0qbVQhHq2KtTRG63Wxt4ghl9sWgcGkwlOzt7mmaMSmlpKRUVFbjdbtxuN4OD\ng0xMTNDa2sof//hHXC4XVquV+Ph4Nm7ciMlk4tixY5pCr79Ynv906sW+N2fi7Fk4dWphAkbvugt+\n/GM4fhw2bZr/9hYrkRawywSeAq5DGezLUAJEfyKEcEspdapLeIFwZNsn/w8qzS6l/D7wfb37Feuo\nMSAOh0N7eAwODmIymejo6OCdd97RaszExcWRmJjIFVdcQWVlJRMTEyHrjSwksTjfHMrjkJmZycTE\nBKdOncJisXDFFVcE9Ub4BwXW1NRgNpvx+Xzk5eXh8/mw2+3U1NSQnZ1NRUUFLS0tmM1m9u/fj9vt\n1t421UE/MJh0w4YN2pvtfBGLxqHBVGbyNJ46dYqGhgYGBgbo7u7G4/HQ0dHByMgI3d3djIyMkJSU\nhNVqZffu3RQWFtLd3U1/fz8bN27E4/EAwcXyYpU//EHxSlx//fy3df31kJMDP/2pIuVuEJxIPSEP\nowSjFgL+gmFPAt8CdDdCDOaOGnT6yiuvMDg4SH9/P6Ojo9hsNoqKijh9+jSdnZ1MTEyQmpqKyWRi\nzZo1fPCDH6SsrIyXX355xnojeqRyhvM2HYuVO2d6uGdkZFBYWEhcXJwmNAZMqdejarW89dZbnD17\nluuvv5729nZOnDihxWT09PRw9OhRVq5cSWpqKps3b2bPnj309vbS2dlJS0sLNpuNgYEBTp8+zfHj\nxxkfH8dqtc7qgZmJmRQ1/YlF43C5EK4XKtDbNT4+zmOPPUZDQwNxcXGkpaWRnZ2N2WwmJSWFEydO\naPFMExMTDA0N0dfXR29vL21tbdjtdjIyMnA4HGzYsIH3vve9IcXyYpFXXoGqKph0VM4rFgs8+CD8\n6EfwX/+lb42apUSkRshNwA4pZXvAhd+EEgxqEAM0NTWxf/9+Tpw4QV9fH/Hx8cTFxdHT00NzczMD\nAwOMjY0xMTFBd3c3KSkpXHrppdoDq7W1dUq9kZaWlinVdCcmJnjkkUeora3V3vo/+MEPzmkgCudt\nOhar8Iaayuju7iYtLY17772XAwcOUFtbi9vt1hRS1SBTh8PBwMAAo6Oj9PT08PTTTzM+Pk5ubi49\nPT2MjIwwPj7OqVOnaG5uJj09XStQl5OTw+HDh6muriY3N5djx44xNjZGY2MjAElJSeTk5ERsrIVr\n9MWicbhciNQL9dhjj/Gd73xH06+prKxk1apVWK1W+vv7kVIyPDysTc2oeL1exsbGGB4epqqqCqvV\nyvr167WXjsVgfEqpeEIefHDh2vzIR+DrX1cCVP/8zxeu3cVEpEZIMjAU5PMMYF5k0KOVbZ9t2VLE\n5XJhMpkoLS2lqamJ/v5+MjMzsdlsOBwOLBYLw8PDxMXFaRLN1/hNlgbWGzGbzVMefOPj49TW1jI8\nPIzP56O1tXXOb8PhvE0vpnLx/gNzoOhaa2ur9l2dTif9/f0MDg5y6aWX0trayujoKNu3b+fFF1/U\npLNNJpP2ppqTk4PFYqG6upqenh4sFgtmsxmHw4HZbGZkZISsrCxsNtsUXZK5Eq7RF4vG4XIhUi+U\nagRfdtllHD9+nKSkJG6//XYGBgY0g/nUqVMIITCZTJohomZ9mUwmRkZGuOyyy+Z9uk9vTpyAzk64\n4YaFa7OsDK69VvGGGEZIcCI1Qg4ADwD/NPm/FEKYgM+hBIPOB1HJtgshikMtm6f+XnSysrJIT08n\nMTGRFStWYLfbSU5OJjExkdWrV+NwOBgaGiI+Ph6r1UpFRQU5OTna9mp9ETUmJDk5mTfffFN78A0P\nD2OxWPD5fAwMDBAfHz/nt+Gl9jYdql5PQkICRUVFNDQ0UF9fT35+PhUVFVol44yMDE6fPs3Ro0fJ\nyspi5cqVmidqZGSElStX8v73v5/e3l4OHDigeafa2towmUxYLBbi4+PJ83tY3gAAIABJREFUzc0l\nLS2NlStXRjxAhGv0LSbjcKkR6X2j1is6fvw4CQkJbN26lauuugpQMmk6OztJT0/ntddew+fzaUaI\nyWQiMzOTrVu3cuutt7J58+ZFZ3S+8gokJMDk110wPvYxRZ31nXeUAncGU4nUCPkc8AchxOUoEu5f\nBy5F8YTofop1km2/a4ZlC0YkGQXhbBNsnbKyMu6//36OHj3KxMQEHo+H/fv3c+bMGU2KWRXISk9P\nJzMzkyeeeIKCggLWrl2r1SvZvXs34+PjXHbZZSQlJbF7927S09PZunUrDoeD2tpaMjMz2bZt2zRN\nDLVfTqeTgYEBTV9AfYBJKcnJyUFKSVpaGkeOHNEKtfnHl8z1uAWurweB+1Q1El566SX6+/vZsmUL\nN954I1JKzp49qx3z3t5e0tPTSUpKory8nJ6eHs6dO0dDQwM1NTVMTExw/fXXc+utt3LkyBGGhhQn\nY1FRET6fj4SEBOx2Oz09PWRkZFBaWkptbS0+nw+TyURaWhper5fs7GxsNhuJiYnU19fzk5/8hIqK\nCgYHB6ccd/9jOltMj/93Dpb2vZjehJcSkXqhHnzwQaSUvPnmm+Tk5HDllVdSX1/P0aNHOX78OI2N\njfT29mqeOFB0hxISEhgZGWH//v0cPnyYpKQkrrzySh566CHWrVsHEPNS/3/4gyJQlpi4sO3edRcU\nFir1ZB5/fGHbXgxEqhNSK4QoB/4W8AA24LfA96WU53Tsn4oesu2zSbovCJHM5YazTah1KioqqKio\noLGxkYcffpg33niDzs5OrUqmyWQiOTkZn8/H3r17MZvNxMfHs2HDBjo6Oqivr6e3txeTycTRo0fZ\ntGkT69cr+nTq9iaTCbPZTGtrK6dOnQpaOba9vZ3m5mbWrFlDQUGBtnzPnj14vV76+vro7u6mo6MD\nmC4VPtfjNh9VdINVwf3d737HwYMHmZiY4I033sDhcNDS0kJNTQ1dXV243W7sdjs+n4+WlhZWrFhB\nd3c3x44d4+TJkwwPDxMfH8+ZM2cwmUyMjo7y9ttv09XVRU5ODuXl5YyPj3Py5ElOnjzJ+vXrSUpK\norm5mZ6eHpxOJykpKYyNjVFWVobZbKatrY1z587R29ur1QryP+7+x3Q2eXb/79zX1wcwrRSAwcIT\nqRcqLi6Oq6++mqGhIbxeL0888QTd3d00NjbS1tbG4OAgvb29miEMitaNmh7uz6lTp+jo6ODhhx8G\niGmp/5ERePVV+PKXF75tsxk+/Wn4zGfg3/8dVhtRk1OION9SStknpfyqlPIeKeXNUsr/N08GSCjm\nKtseyfY89NBDvPe9753yEygJPhf853K9Xq8m+hPtNrOt43K56OzsREqppdlKKZFSYrPZGB0d1bwR\nw8PDjI+P09/fr71tqwN5X18fO3fuJDU1lbNnz2oxJ4WFhfT29gZt1+v1kpWVpcUsqP3z77Pb7aaz\ns5OMjAwyMjK07I5IjtuvfvUr/vIv/5JHH32Up59+mkcffZQf/ehHsx7n2QjsQ2trK52dnVitVvLy\n8hgYGKChoUHTCRFCMDw8rGkpJCQkaN8TlEwFIQSJiYkMDg5y8uRJ3G43VqtV++ns7JxyXHp7e+nr\n6yM5OZmCggLGx8fJyMjAbDZTUlKCyWRCSklJSQler5fx8fFpx93/+6h9DXbMA7+zqicxl2vXIPYI\ndt9ZrVZMJhPx8fHaFMxsXozx8XEcDod2L892LV1M9u2DoSG47baL0/5HPgKpqUqQqsFUIhYrE0Kk\nocRk5BBgzEgpfx5lvwKJWrZ9lmUh0Vu2PZK53HC2mW2dzMxMEhIS8Hq9jI6OakFmQggGBgYwm80A\nWgbG0NAQKSkpWkl5r9dLcnIy+fn5WhurV6/m2LFjM2piqP1qb2/HarXicrkoKCjQ1lP7nJ6ezsTE\nhOYJyc/Pn7KvuRy3++67j82bN0/zhPzDP/zDrMd6JgL7UFRURG5uLk1NTQwNDWlxHi0tLbS3tyOl\nJDExka6uLu3Yr1ixgomJCc6dO0dcXJym25Kdnc26des0qez+/n7sdjuFhYqzzv+4FBcX09zcTFtb\nG/Hx8QwODmrfMTc3V0vZBUUszWw243Q6WbVq1ZTjFqhpsmXLFjweD4cOHdJc/P7fOX1S53qpxO8s\nV9RzWldXx8TEBFJKenp6mJiYQAihTb3MpjsZFxc35T5NT0/XajQF3r8XmxdegOJimJw5WnCSk+Hz\nn4cvfQk++UnwE6Ne9kQqVnYb8EuULBkPU+vFSEBXI0QP2fZZli0YkczlhrPNmjVrSEpK4p133iEr\nK4vm5mY6OztpbGxkdHSU+Ph4Vq9eTUVFhfbwUQPP4uPjsdvtSCkZHBwkKysLs9lMfn4+iYmJOJ1O\n0tLSuOGGG7jrrrvo7e0lMzOT06dPMzQ0pFV83b59+7S+qaqMFouFiooKysvLGRoawul0kpWVxY4d\nO+ju7iYzMxMpJceOHQOYpvap/q3GlqjaG6HiEqKpohss/kT9XA3crayspLS0VDt+vb29VFZWaoF+\nx44dY2JigtOnT2vp0OqUxqZNmygpKSEpKYn6+npNPv/gwYM4nU4mJiZYtWoVJSUl5ObmkpeXR2pq\nKk6nE6vVSllZGTfffDNer5eenh58Ph+dnZ3k5eWxZcsWAA4fPkx9fT1paWn09vZqhfACY3ZUTZP4\n+HhKSkq0rB51usX/OAaLCTFYfKjnrbq6mr6+Pmw2G3a7nWuuuUbz2j3++OOcPn0an883zRgRQmC1\nWrV79tlnn2X9+vVccsklZGZmTimCGQtICc8/D+9738WVT//kJ+EHP4DPfQ6effbi9SPWiNQT8v8B\n/wt8UUoZLFV3PohKtn0WSfcFI5K53HC2eeWVV3jyySdpaWnB6/Xy8ssvU1JSQlNTE+np6doAmp+f\nT0tLC6Ojo7jdbk2QSB3UhRD09/dz5swZEhMTSUlJobi4mM9+9rNThMoaGxvZs2cPLpeL5ORkxsfH\nNel3f1RVRtUjMTQ0NOX/nTt3snXrBSHctWvXzngMAI4cOTJrbEjgMZuLERIs/gQuxK9YLBZaW1s5\nevQoNTU1DA0N0dbWRkJCAnFxcezatYt7772XxsZGTpw4QX19PS0tLfh8PpKTk9m3bx9JSUn09PQw\nOjqKEIK2tjZOnTqFyWQiIyND26fZbNamySwWC2+//Tatra1s2LCBtWvXkpGRweuvv865c+c4f/48\nY2NjPPDAA6SmppKUlITdbuell16iq6trWgE8f00T1Rjy+XzT0j6NDJilhXpvuFwu2traqKio4ODB\ngxQXF3Pdddfx+uuva1OywbwhUkp8Ph/nz5/H5XJx/PhxioqKuOqqqygoKKCqqiqmrpeaGmhrg1tv\nvbj9sFoV0bJ771Vqytx558XtT6wQqRGSD3xnAQ2QqGXbZ1u22GltbcXpdGIymRgcHMTj8eDz+Rga\nGmLTpk3U1dVpmh7d3d1a3j8w5UGjihWBIlA0MTFBY2Mjzz//PD09PQghqKysxOVyYbFYSE5O5sSJ\nE2RkZFBbWzvtARSoZ+CvlRGJyuZCqHQGa0M9HmvXruXAgQO0tLTgdDppaGjQPCEpKSnaHLn6kG9t\nbaW/v5+RkRFNN6S9vZ3R0VFtqkuNC1ENkqGhIYaGhpBSkp2dTWdnJ0NDQ1x77bWcP3+e+Ph4Dh48\nyHPPPUdiYiKnT58mISGB7Oxsenp6qK6uBtAUcT0eD9nZ2TQ0NJCdnY2UUpPptlgs1NfXa2J2fX19\n1NXV0d/fz9mzZyPKgjFqyiwO1GmZgwcPcurUKXp6eqirq6Ourg6n06llxwRDjWeKj49ndHSU/v7+\nKXFHsWSEPPcc2GwLUy9mNu65B379ayVt96qrIDf3Yvfo4hOpEbIHuJypmSkGF5GioiIsFgttbW2M\njo4yNjZGR0cH8fHx1NUpmm5SSk2YTFVMnAnVWzI8PMyrr75KXV0dNpuN2tpatm/fTn5+Pg6Hg4yM\nDG644QYSExOnPYCCxVGoWhmRxBUE7i8zM3OKiqseA16o+BN/ETJV7bS7u5vR0VGys7O1QF51/ays\nLOLj4xkYGEAIwfj4OIODgyQlJTE2NjbF+BNCIITQ5ujNZrM2CMTHx2MymWhtbcVisXDixAlOnz6t\n6YOoonTnz5/HYrFo6bvvvPOOpn65e/ducnNzNQXd1NRUTUjN7XbT19enZRCp8UFnz56lq6sLmFsW\njFFTZnGgTpfs27dPe8Foamri9OnTWlXcmZBSaoZzSkrKtHivWOGpp5SAVD+n5kVDCPjhDxW9kDvv\nVNKGl7uce6RGyIvAN4QQlwA1KHVkNKSUz0XbMRWhjCjfAXYBE8Ajk8Xogq0bqKr6ISnlycllrcAw\nMIIyHfOfUsrf6NXPi82NN95IW1sbP/nJT+jo6NBKxPt8PlJSUsjIyODUqVMMDQ2Rnp5Ob2+vFqSq\nEh8fP80FazKZtIBEq9WqRb7bbDZ27dpFbm4utbW1JCYmYrVapz2AAmM5kpOTqaiomKYXEi6BsR5S\nyjkNeKo2xkxGy0wxOKoI2UsvvcS5c+dYu3YtXV1drFy5kpKSEnbt2qWtX1ZWxvbt2zl58iRJSUmM\njIxoAcJ1dXWMjo6Sm5tLQUEBeXl5dHV14XQ6KSkpYXR0lPPnzwOwbt06br75ZjIyMjh8+DCHDh3C\n6/Vit9txuVwIIbj88stxuVzk5uaSkpKC0+nU6gKpxtHOnTtxOp243W6tLozdbsdut9PW1qZ5flQj\nKZa9VQbR4z/FWVdXR1NTE8nJycTFxWG1WhkcHJxx+8TERPLy8ti1axe33HILdrudwcHBWeO1FpKT\nJ5XpmH/7t4vajSnk5MDvfgfXXQd/8RfwxBNKnZnlSqRGiJrvGCzrWgJxEe43GPcDa6WUpUKIdOCo\nEOLVYJLtTFdVfYwLqqoTwD1Syhod+xYzmEwmPvzhD7Nq1SoeffRRTpw4gdvtZmhoCJPJRHd3Nz6f\nD6/Xi8lkwmazMT4+rk3JWCwWbDYbcXFxWmzHyMiI9nZutVoZGRmhp6eH/Px8srOzNWnwqqqqkEGz\noWI5du7cGdbAFMy17x+joA7I4Q54Z8+epa2tbUajJVQMjvqZv5FmNpuxWCxa1Vr/B68QgnXr1nHt\ntdcC4HA4WL16Ne3t7fT09GgiZgUFBVrwsNfrpaOjg6ysLD7wgQ+QnZ2tVTDeu3cvTz31FMPDw4yO\njuJyuTCbzSQnJ+P1eikpKWHr1q0MDg7S0dGhGZnx8fEMDw9z6NAh1qxZQ0ZGRkgvz3x4q2Ltzdhg\nKmVlZezatQtQXhby8vLIzs7mj3/844xTMmNjY1RUVPDJT36S8vJyGhsbp2jKrF+/fto9sdA8+aSS\nGrtz50VpPiRXXKFMy9xzD9xxh/K33X6xe3VxiFSsbCHrud/DpNEjpXQLIZ4E7iPAAApDVVUQpjbI\nYkUIwU033URPTw8vvPACLS0tNDQ0aEGQK1eu5Oabb+att96itbUVIQRSSq1cd1FREevXr2ffvn14\nPB5SUlJIS0ujsLCQnTt3alLgwSLfpZRanIQ66PhnUYR6O54tfmA21/5cBzzVAxTNW7p6nIuLi2dV\nsfV4PJw6dYq2tjZSU1Mxm81MTExwxRVX0NfXh8fj4fTp03R3dzMxMUFfXx9paWlIKVmzZg333Xef\ntr/W1lZACd49cuQIQgje9a53YbPZKCkp4YYbbuA973kPf/jDH3C73Zw8eZKOjg6SkpI0wykjI4Pt\n27eTkpIS1MujKsEGfre5YNSUWVz4X8/V1dXU1NRw/vx5ampqNIG6wPXj4uIwmUwkJSVNOd+qh+7g\nwYO43e6IpvP0QkplcL/jjtiYignk9tuVrJ2774bLL4enn4YNGy52rxaeSFN0Z9Kdk1JKPZ1fwZRO\ntwRZLxxV1Z9PTu8cBv5RShk7ajo6IYRg8+bNWoE0i8WC1+vVCqDl5OSQmJhIfHw8eXl5nD17FovF\nQmpqKnFxcTQ2NmpeD4vFgsViYc2aNaxfvx673T5twA2miGqZ9C36K2uGMhZmMzJmc+3PdcBLS0vT\narpE85YeTsZSU1MTBw4c4OzZs/T09GiF6IQQ9PT0AEq9jo6ODoaHh7WMhJSUFGw2G+fPn+eNN97Q\n5O5Vef3m5mZNz6GtrY2CggJuuOEGduzYQWNjIw0NDQwODpKYmEh2drYm0X/HHXcwMDBASkrKlIwk\nYNp3iSYjxqgps/hQz5nq2Xz11Vc5fPiwVlnXHzWoPT4+XhPlgwv3uKqaumHDBjwez0Wbjjt8GBob\n4bvfXfCmw+amm+Dtt+H974ctW+CRRxRhs+UUxx3pdMwdAf+bgWJgDGgGwjZChBCHgNLAj1GmdYKp\nhM3l9Pivu11K2S6EiAO+ihI7cssc9rVoULU5enp6kFJisVjIycnh9ttvJzU1lZ6eHm1KIj4+Xgsw\n6+7uJjk5mby8PJKTkykrK9MK073++uuMjo7i8/nYuHEja9euJTs7G6fTqSminjhxgqysLM6cUWxG\nNe7A5fr/2zv3MKmqK9H/FtDKo1vegtAtNA8RFbkao0EwzmgmYpRxEk0cNRONr4ljBiWZOGqumtEx\nGj8/jZObxDg3aqJGM+qN72fURGnwEUHkod08JIKtQNsNdAtN0/S6f+xTRVHU69Q5Vaeqev2+73xd\ndU7ttVefffY56+y91totzJgxA9jbWMhmZGQb6fD7wDvwwAM59NBDi/KW3tLSwubNmxk3blw88mTK\nlCnMnj07PuXV3NxMc3MzbW1t8eRmNTU1jBkzhpaWFh5//HFWrVpF375uhnPw4MGMGDEiPs0ybNgw\nRo4cyaBBg2hqauKVV15h/fr1DB8+nKFDhzJz5kyam5vZtm0by5Yti68RZBipiPWnDz74gH333Tce\n/ZJMbHXnxFW3Y31p//33Z9myZWzdujWln1ixuOsulyK9mKvm5sNBB8Hrr8O8eXDxxc5Z9Ve/ctNI\nvYF8p2OOSN4nIvvhfDD+4FPWXmG3SXI/xGU3fcPblS7Tacasqqq63vu7S0R+CjTmot+8efMYnHQ1\nnHXWWXsMk5casdwcAwcOjK/1kjg3G0v/vWLFClSVoUOH0t3dzfbt26mtrWXr1q1UV1fH15Rpa2vj\nvffeY/jw4WzcuJElS5Zw9NFHU1tbG1+VMzEjaqrMmonGQuIUTGKYaCojI5+h/QcffHCv1PqxTI7F\nfEuPrWK8bt06qqurqaur4+STT+bLX/5yfCrs+eefZ9iwYbS3t8fP5dSpUxkzZgxdXV2ICH/605/Y\ntm0b3d3dDBgwgEmTJsWNvp6eHurr6+MRMLERqdbWVvr374+IMH78+PjIi2HkQnV1NfX19TQ3N7Nx\n40Z27dq1x/HYmlNLly7loYceii9Yl4ufWDHYssVNxVx9NfQppvNAngwYAHfeCSee6EZCjjjC6X/0\n0dnLljt5p21PRlW3esnAngTCXCvwYeAiEXkEGAycSYoRjExZVUVkIFClqrEJzrOBxblUHnba9mKw\nadMm1q9fz4gRI+jq6qKurm6Ph+6UKVOYN28eixYt4vHHH+fdd9+lvb09PiIyffp0pk+fzs6dO1mz\nZg319fX87ne/i8tMzAlQXV0dj7qITRuk8glJJHEKJhYmmjjVk0g+RkMqI/GBBx7gm9/8pt9TGYjE\nVYxh98qi4JK9xebfx4wZw/bt2xkxYkQ8/8Knn34KuMXDdu7cybZt25g8eTJtbW0AjBs3jp07d1JX\nV8esWbPizqmzZs0CYMKECdTX11NdXc26dev48MMPmTp1Ku+//35ctmGkY+TIkYwZM4YxY8bE88p0\nd3fH15c54IADEBHmz58fz0EUW7CuFKbj7r8fduyAb387MhXy4utfd/4hZ53lVvy97z4488yotSos\noRkhHoO9LUzuw+UkWYmLcLlVVZdDPH38HFW92PttclbV2CU4CnhURPrgpmjWAN8KWc+SoaOjg9Wr\nV7N8+XL69+9PR0fHHsdjN4mJEyeyZMmSeLKxqqoqampqmD59OgcffHA81XhHRweHHXYY27Zti6+y\nG8sJEIuSCRLCWVNTs5ePQiUgIvFVjGOjPwsXLoz7pDQ1NbF+/Xpmz569xxTJrFmzaGxspK6ujrq6\nOgYPHswLL7zAjh07qK2tZfbs2QwaNIiqqio2bNjA3XffzfTp0xk0aBCNjY3U1tZywgkn7GHwbNy4\n0aJVjJyZPHkyhx9+OK+//joDBgxgyJAhdHd3069fP6qqqhgwYEDcQbVv3760traWTCh2dzfcdhuc\nfjqMGRO1Nv6pr4dXX3UjIv/4j/DJJ3DZZVFrVTjydUydm7wLOAA3CvFcUKUS8aZW/tXbko89iRt5\niX1Pl1X1A1L7l1Qk1dXVTJw4kREjRtDS0kJ1dTWwO0dG7M28o6ODt99+m/b2drZv384RRxzB4MGD\nWbp0Ka2trXGjpLu7m9NOO43x48fz6aefxkc88snzAZUfwpkq4idx9Oejjz6Kh/WuX7+eZcuWxY2V\nxsZGGhsb2XfffePZZ2fMmMFRRx3F2rVr4+G6r7/+On/84x9ZsWJFfI2gM844g4kTJ8anelQVEbFo\nFSMl6SLTYvtjeWz69+9Pa2trfEpw9OjRVFdXx1fKbW1tZdSoUSXTjx95BNasgYfLOAvUPvvAvffC\n6NFw+eWwcSP8539WpsNqviMh85K+9+AWhfsNcFMgjYzAjBw5ktra2vib88iRIwE3DXLffffFvde7\nurro6upixowZLFy4kF27dlFdXR2fInnttdfo6upi7NixNDU1MWHCBGbOnBlYv0p/KKaK+Ekc/dm0\naRNdXV20t7czbdq0uM9OutDYPn367LFuDxCfFtuwYQP77LMPW7dupaGhgc7OTgYPHhxfDblUhseN\n0iNdZFpixFtnZyefffYZ27dvp0+fPvTt25dRo0ZRXV3NqFGjGDt2LM3NzRx//PEl0Y97euDmm13U\nSZnNou9Fnz5wyy0uudkPfgCbN7tIn3LwcfFDvo6p9WErYoRHuod8S0sLbW1tDBs2LP4dXO6MWMKi\nQw45hPfffz+enjxmkISZ+bLSH4qpIn4SR3/Gjh27lx9MLMwx1/MyefJkTjrpJFauXMmWLVsYNWoU\nVVVVe2RDLZXhcaM0SReZFtsf8y/q7OyMj5j069ePrVu3MnTo0PhUYV1dHUceeWTk2VHBZR9dsgTm\nz49ak/D4t3+DIUPcejObN7sREm9lhYogbJ+Q0PGZtv0O4O9xETT/S1XfTTiWnNL9vDRZV8uedA/5\nWLRGLFJk2rRpTJw4ke7u7vgwv4jE38ZjvguVOm1SKFJNN6UyDIPctEWEb3tedy+88AI1NTWMGjUK\nEbH2MnIi3bRobH/Mv+ikk05i7dq1PPvss/Gp2NmzZ1NfX5/W+TwKtm930TCnn+4Wh6skLrzQGSJn\nn+0ifx5+2EXUVAIlb4TgL237w8BPgFR2cHJK99+wO6V7RZFurjddtEbywzAxlDZI5szeSjqDI9Mo\nRz4rz/bp04fzzz+f4447jpaWlrhzayk9GIzSJd2Iaar9Bx10EPX19XtEwYkIM2bMKIkREIDrroMN\nG9x0TCVyxhkutfvXvubS0D/xRGXkEikHIySntO3e8fkQHz2Jk0NK94oi3VxvYrRGLlT6tEmhyOe8\n5bvyrLWRkS/prp1MayfBnmtAJe6PkgUL4NZb4aabYFJy6ssK4qST4MUX4ZRTXA6R+++Hz38+aq2C\nUQ4uLqnSth/oU0amlO4VR+Jc744dO+K+H7A7QmbBggU0NTXtlZI5FfmUMfzR0tJCZ2cnNTU1NDY2\nsmjRopTpsq0djGIRu94aGhp4/vnnaWhoYNGiRXR2dqa8t0TFJ5+4XBrHHAPf/37U2hSeY491GVb3\n2w9mzIBLL4Xm5qi1yp/IR0IKnLY9Y9W5/KgcM6ZmCoHN540737f0qMiUMbVUiUW7NDQ0ALBs2bJ4\niG6McmsHo7zJtC5Uqfgdtbe7Beq6u11obr/In2jFYcoUN/pz++1u+umuu+Dkk90IyYwZbjRo4MCo\ntcyNyJssxLTtmciY0j0T5ZgxNVMIbLa1WlKRT5koKZWMqX6YPHkyhx12GG1tbWkX/iq3djDKm9j1\nlrgulIhQV1fHgQceGLnf0ebNcOqpsHy5W29l7NjIVImEqiq44gq33swDD7jt0kshlmF/xAiXrO2A\nA9w2erT7W1vrpnJqa6PVP0Y5TMfE0rb38RxTzwR+70eAqm4CYindSUzpno9CyW/ZQSiErNic7rHH\nHruX42muicIS9QqaXKxUz1cx5OZah4hw5JFHMmXKFNrb21Oe52ztEPX/UA7yw66jkmXFrrfXXnst\nvi5ULIleqntLMXV79104+OAHWb7c+Ujk4xcRVI9SKT9kiDM+Fixwhtmrr8Jvf+uyrM6a5UZE3n8f\nHnzQ5Rs5/XSoq4ORIx9k7lwXztzTk6WyAugfJ5ZZsVQ3nKH0M9zqvCuB7yYcmwPclfD9TtyoRxfw\nMdCUcOwgYAFu4bo3gUOz1HskoG+//bYmM2fOnL325UuxZfX09GhjY6M2NDRoY2Oj9vT0ZJWVa5kg\nehVa1v3336/p2jOIXD9kqyPbec52vBT+h2LJz9aeYdTR22XFrreZM2fqc889p/Pnz8+r/+eiW67t\nuWWL6pVXqvbrp1pTM0dXrQpXj0ov39Oj2tys+uijquPHz9ExY1RBddIk1dtvV21rC6/+t99+W/Hc\nKjTDszby6ZhsqL+07d/JICdlSvfeRj7RFBaBURyynWdrB6OYxK63YcOG7ZWxt9isWgV33w2/+AV0\ndsK118Ibb8DEiZGqVXaIuCmZr33NJT177DE3EnLnnW6U5Ic/hHPOgUsucSv5FoNymI4xQkQtwqIs\nsXYzoqaY12BnJ/z5z3D99W5V2cmTXcryCy+E1avhmmsqL315FPTpA1/8oss0u24d/Pu/wzPPuJT3\nxxwD99wD3sLdBaPkR0KMcLEIi/LE2s2ImmJdgzfcADfeCDt2OH9rjABzAAATrklEQVSHE05wD8dT\nTimfiI9yZPRoN8J09dXw9NNudOSCC+Cii1xY8Be+ANOmOQfg4cOho8P5mgC4SR3nWzJ8uBttyRUz\nQtLTH+C99/ZOzLplyxYWLVoUSiXFlrVkyRJWr15NfX09q1evpqGhgY6Ojsj1KrSspqYmIHV7BpHr\nhyB15NJupf4/hCk/W3uGUYfJ2lNWrveOfHRLbM+hQ52j5VFHuVDTvn3db2IPvExyguph5R11dc4Q\nvOwyN12zYIFLjPbxx3tIYOrUvcufcw5873t79M3+mfQQG9ZNjYicDTwQtR6GYRiGUcaco6q/S3fQ\njJA0iMhw4CRchtbOaLUxQmB/4FTgKWBjxLoYwbH2rCysPSuP/sB44HlV/TTdj8wIMQzDMAwjEsy/\n2DAMwzCMSDAjxDAMwzCMSDAjxDAMwzCMSDAjxDAMwzCMSDAjxDAMwzCMSDAjxDCMnPDC1g3D6MV4\nK9qHZjuYEVKmiEhfEblERF4VkbXe9qqI/IuI9A2xnhvzKPMtEblWRI5M2n+VDxlVInKZiMwVkX4i\n8g0ReVxEbhCRffzqVGyK0T5hnGefvBWmMBH5BxEZ5n0eISIPi8iHIvKkiIwNsy4jN8K8bkVk36Tv\nZ4jIHSJyXqhKF4Gg5yXouSiB+qtF5BYRWQfsAHZ4ffUWEanJRUZa2ZYnpDwRkV8Bo4E7cQnVBBgH\nfAfYoKoXh1TPh6p6oI/f3wzMBN4BzgBuVtU7vGOLVPXITOUT5PwSGAUMADYD+wK/B74KfKKql/v6\nR4pModsnrPOcQX5rit2DgS0AqjosiHyvjuXAYaqqInIv0AzcD5wC/I2qnhK0Dq+evsDxQOw6/hD4\ns6ruCkN+EERkAgl6qeqaiPUJ7bpNvA5F5BJPxoO4pGTPq+oNeegXyfkKel6CnosSqP8R4GPgF179\nAPXAJcBYVf1apvIZUVXbMmzAROAVYA1wG9A/4djCEOtp8vn7lWn2S7pjGWQtSrMtBjp9yloK7ON9\nHg28CVztfV/sR473tz+wFRjofd8ndqyU2zXM9inkec4g/2XgV8AE3M1uPLDO+zwuqHyvjhUJnxcn\nHXsnpDqO8/R+HWfE/g/whrfviz5lfT3h8wjgaZxR9ifgQJ+ypnpt9rGnT+zzm8ChEeoV5n1lccLn\nN3APK4Aav3046PkKeo6Cnpeg56IE6k/7fMp0LJfNFrDLzi+AR3A3sbnASyIyW1XbybIwTzIicniG\nw36HtFRERqrqpqT9I3EXph8mAGcB25L2C+7G7QdR1S4AVf1ERL4EPOu9jfoZdtvpyegUkTWqus37\n3iUi3T51SkVo7ZqGMNsnFWGd55So6gkiMg+4G7hYVZtEZKeq/jWo7AQ2iMgMVV0IrBeR0d7/UgOE\nNaX4c+CrqvqXxJ0i8nnc/zbNh6yrgIe9zzfhDMELgLOBO3CjdLlyL/ATVX00Sa8zgHuAoyPSK8zr\nNvE67KuqHwGoanseffhegp2voOco6HkJei6irn+XiExW1ZWJO0XkIKAnh/JpMSMkO/ur6s+9z+eK\nyNW4B9bf4f9m/w67h9KS8ev09xPgHRF5HIg9GMYBfw9c51PWYmCLqi5IPiAiXT5lfSYi41V1LYCq\nbhWRk4DngUN9yBER6atuyPy0hJ39COe6DbNdUxFm+6QirPOcFlW9XUReAO4RkUcJx3hKZC7wmIgs\nBDYBb4jIK8DngR+HVEf/ZAMEQFXfSp4nz4HE//9o4Ejv+rxNRM71KWtI8gPV0+sR8e+HFaZeYV63\nB4vIIk+/CSJS4z30BKjyKSvo+Qp6joKel6DnIur6fwC85smI1T8eOAK4MIfyaTEjJDsDEr+o6o+9\nB/NL+B+9+CswS1Wbkw94Dj85o6q/FpGXgdPZPUe6BjhOVT/wqdd5uCmPVBzkU9aVON+BOKraISJf\nBi7zIecSXOfYlfT2PQ74L586pSLMdt2LkNsnFWGd54yo6nIROR74EW4KIzRUdamIHIZ7Gz0EiN1g\nr1HVsOpaLSLXAneq6kYAEdkfd335bYf+IjINdyNX3dOnxK/h2iIi/wQ8oKo9nl59gH8C0i72VWi9\nvOv2JZyfUdDr9uRk8d7fUcAvfcoKer4CnaMQ+nOgcxFCuwSt/ykRmejJidX/EvCcqnbkUH9azDE1\nCyLyB+BXqvpc0v7vAbeqas4RRiJyB/Cwqs5PcexOVf1OYIWNnAizXY3SRURGAjcD38C9dCmwCzc0\nf2XMMMlR1lrc0HPsrfo4VV0vIoOBV9SHM7CITML53HwO59sAcADOF+s7qtoUhV6lStDz1RvOUbli\nRkgWYkO2qrojxbGxsbm1YuPN/V+M8+VI9Pp/CPdwzdnzvxRlZZDzIHCXH53SyC9ou4Z5TiOWfzZQ\nF7b8pDoK8j+kqG8YgKqmivwJIncgMCqfES7PSIqd33Up5vwj0SuNvFNV9akoZYV9vnI9R0Gv1UJe\n6yJyo6r+sNDlReRbuCmYJ1V1ccL+q1T1przrNyMkNyTE0LAwZEm4oXQlJytMnbLUU5CQv0LrX+7y\ni1WHER4i8t+qelGpyfJZb179Pei1WshrXXymUcinvBQwJYAZIVkQkUNwntl1OMtVvM/rgG+r6vKI\nZK1U1ckp9gsuZGqvY+UkK0yd0sgPrS3SyC+0/mUtv1h1ZKn/KVU9NSRZd4VoGEcuq9ReurLIb1LV\njL5rQft70Gs1hPKL0h0Cpqpqxoi+EMovBT6nLjpxNPAE8JjnS7dYVY/IVD4T5pianXsIL5QuTFmh\nhtKVoKxCh7iG2RapKLT+5S6/WHVkIowopRhPVoIsEZkK/Iakh7U4x/nzVHVFRLKCpjcI2t8Dh8gG\nLB80jULQ8gVLCWBGSHbCDKULU1aYoXSlKKvQIa5htkUqCq1/ucsvVh1pUdW3Q5QVmuEQsax7Sf+w\nvhd/xnmYsoKmNwja34Neq0HLB02jELR8wVIC2HRMFkSkATePlyo07J9V9dgoZHllx7NnyNaHwKN5\nOsiVnKwwdUohO9S2SFPHeAqkfyXIL2Id9bi38bdUdXvC/r9T1RdDqiNSp01v1CHZ6fH3fqcVRaRR\nVaf4PVYEWR8AMzVNegNVrUtRLPE3gft70Gs1SHkRGQdsVdW2FMcGJF7XBSr/t0Crqi5J2j8IuExV\n887rY0ZIFiTcULrQZBnBsLboHYjIOcBPgU+AIcA31GVoDexQl1RPIOfAILJE5FJcMqnfs6fT45m4\ncPP/40NWSb50ScD0BtbfSxczQnJEQgwNC1NWGvmRh9IVUlbIOhW0LdLUGZr+lSg/zDpE5B3gVHU5\nIb6ES9V+nqq+7NehTkRuS3cIOF9VB6c5XmhZTcAxyW+5IjIUeNOPg2+lv3QVor8HvVZ7e3lLyJQj\nqrpJVRd5W6ALN0xZaTgt+0/KWlZoOhWhLVIR5jmtRPlh1iGquh5AVf+IW6H315Jfev5/ATpwC58l\nbpsjltUn1TC7J8/XPV5VV6nqicBk4Bxvm6yqJ/g1GsKUFRYF6u9Br9VeXd5GQgIgFkpXcFlh6uSj\nzjDbtdDhiWUtv9B1iAstPFbdwoSxfYfgVlHdT1VzXrNJRN7CjVIsTXEsq19CAWX9DDgY+G/2dHq8\nCHhfVf81V1nlSND+6qd80Gu1t5dPhUXHBMNC6QokK0yd8iBwuxZa/3KXX6w6gLuAo4BXYjtUdYWI\nfAW3eqofrgPSOfD5XSguTFlzcX4W32JPp8f7gft8yipHgvbXrOWDXqu9vXxGVNW2MtyAN4DTU+w/\nAzcPXNaywtSp3NunEuVH1cbA2F4gq7YQ5643b0Gv1d5ePtNm0zE5EFb4W5iySjiULhRZYeqUoY7Q\n2jWF7ILqX+7yi1VHCrkbVXV/k1V+BO2vQcoHvVZ7e/lMmGNqFrzwt2eBfXHW4Jve56dF5LtRycJb\n2toLeYvJ7yMi5+J/KfBSlBWmTnsRclukoqD6V4D8YtWRTJiZWHuDrJIgaH8Nob8HvVZ7e/n0FGMo\nq5w3oAkYmmL/UGBlhLImAS/hvOnf87bNwMvAQeUuK0ydCt0WEelf1vKLVUeKOjearPLbgvbXEMoH\nulZ7e/lMm03HZEFEVqnqpBT7++Au3olRyEooW5L5S8KSFaZOSXJDb4s09RQ6J0xZyy9WHQl1leS0\nR6nKKhWC9tew+nvQa7W3l0+FRcdk51kReZHU4W/PRCgLcHHvQCg37VKUFaZOSYTeFqkooP4VIb9Y\ndRhlT9D+Gkp/D3qt9vbyqbCRkCyIiODC377Bng5NDwP3qZeOuNiyjGBYW/ROSnXEoVRllQpB+6v1\n99LFjBCfiEitehkYS0mWEQxri96BiCzQEBYn7C2ySpWg/dX6e+lgRohP7I2lMrG2MIzyIWh/tf5e\nOliIrn8slK4ysbYwjPIhaH+1/l4imBHinzCHjmwYqnSwtjCM8iFof7X+XiKYEWIYhmEYRiSYEWIY\nhmEYRiSYEWIYhmEYRiSYEeKfVSUqywiGtYURCiJyvIj0iMh+GX5zroi0Jny/TkQWF0fDiiBof7X+\nXiKYEeKTMOPvKz2Wv5ywtjDyRUReEZHbknZnc3x8CDjIZ5lcdMlqAFUCQfur9ffSwdK2G4ZhFBlV\n3QHsyPX3IlKlqjtz+SnOmLEQVKMssJEQwzCMPBGRe4Djgcu8EYhdwHjv8CwRWSIi20VkoYgcmlDu\nXBFpyyRXRP4gIleLyEfA+97+c0TkLRHZKiIfi8gD3qJiiMg43KqmAG0isktE7vaOiYhcJSJrRGSb\niCwWkdPDPh+G4RczQgzDMPLnMmAhbmG0UcABwDrcSMQtwDzgKNyiX0+ISN+EstmmX07ETdl8CTjV\n21cF/G/gcOA03CJs93jH1gExw2Kyp8tl3vergW8CFwOHALcD94nIcb7+W8MIGZuOMQzDyBNV3Soi\nXcC22LLm3mgIwI9U9WVv37nAeuCrwCM5iu8ALlTV7oT67k04vlZELgfeEJGBqrotwdl1k6pu9ere\nB7gKOFFV30goexzwz8Br/v5rwwgPM0IMwzDCR4HX419U20SkEZjqQ8bSRAMEQEQ+B1wHTAeGsns0\n+0C8KZsUTAIGAi96q8nGqAIsIseIFDNCjLSIyPG4OeahsbcqwzAC4ScC5rPELyIyEHgOeBY4GzfF\nM87bt08GOdXe368AzUnHcnaONYxCYD4hRiYagAPMAClfROSnIvIXEekUkUVR61OhdAF9k/YJ8IX4\nF5GhOP+O9wLUczAwDLhKVRtUtQnnh5KsC0n6rMAZG+NUdU3S9lEAfQwjMDYSYqTFGwreGLUeRmB+\nDRyDc2Y0wmctcIwXndLB7pe7az0fjY3AjbiRi8cD1PMhzsiYKyJ3AtNwTqqJ/BU32jJHRJ4Btqtq\nh4jcCtzuOcbOBwYDM4EtqnpfAJ0MIxA2ElLheKF5V4jISu9teK0XqjfOCyk8U0QavDDCpSLyxYSy\nvSLxUbngJcX6LxG5XURaReQTEblARAaKyN1e2OZKEZkdK6Oql6vqL4EPIlS90rkV2IUbcdgI1OEM\ngSuBO4C3gJHAnGQfDz+oagtwHnAGsBy4Avh+0m+acT4jNwOfAD/z9l8DXO/ptAI3pfMV7LpIiYh8\nICJzk/YtFpFrvc89Xt/7fyLymYg0icicpN8fJiLPiEi711d/KyLDE4777s8J9+SvpAv/LjtU1bYK\n3oCfAC248Lx64FjgfNxccg/uzekfgCnAXcAWnA8IuPwHu4D9ov4/bFOAV4DNuHDLid7fncDTwAXe\nvp/jHoT9k8peByyK+n+wzbZy2HDG2dykfYuBa73PsXvnN4AJwE+BrcAQ7/hgYANwAy5cejrOd+el\nBHm++7N3T+4BlgEnAIcCTwCrgb5Rn7d8NhsJqWBEpBqYC/xAVe9X1Q9UdYGq3p3ws5+p6mOq2ghc\ngjNCLohCXyMnlqjqj1V1Ne5ttxMXjvlrb9/1wAhs6sUwCs09qvo/qroGZ0AMAo72jn0XZ/Rfo6or\nVXUJcCHwtyIyKUFGvv35R6r6sqouB84FRuPCv8sO8wmpbKbivOZfzvCbxDDCXSLyF/yFERrF5d3Y\nB1XtEZFPgaUJ+zZ4UZj7R6CbYfQmEvvdNhFpZ3e/mw6c4O1LRHEjHLEF9PLpz2GEf5cMZoRUNtvz\nLBd4IS2jYCSvH6Ip9oH5exlGEHrYe/2dqqTvqfpirN9V46ZJrkgh5+MsMvLtz2V537YbVWWzEje8\nd2KG3ySGEfYFPkewMELDMIxyZxMu7T0AnnN+vY/yi3D+Gn/VvcOi8305jKtD6vDvdMnqShobCalg\nVHWHiPwEuEVEduLyfozEdY6XvJ9dKiKrcIbH94Ah7F6LAmw1zrJGRCYCNbgb6gARme4dWq4BIjUM\no8J5GThXRJ7C+cn9B+Cnv/wc5wPykIjcArTiHFTPBC5Qz8s0AKnCvx8LKDMSzAipcFT1es8A+Q9g\nDG4o8M6En1zpbdNx85RzVLU1UUSxdDWykqot0u2L7f+/wBcTjsUSltXj8k4YhrE3N+H6yJM4I+Qa\n3OrIsX6VsS+q6sciMhMXnfg8sC8umua5BAPET39O/h4L/56Ei9oJFP4dJRLcIDPKES+x0hrgCFV9\nN9vvDcMwjGipxKU0zCekd2NTLYZhGOVFRd23zQjp3dgwmGEYRnlRUfdtm44xDMMwDCMSbCTEMAzD\nMIxIMCPEMAzDMIxIMCPEMAzDMIxIMCPEMAzDMIxIMCPEMAzDMIxIMCPEMAzDMIxIMCPEMAzDMIxI\nMCPEMAzDMIxI+P9qA6IFvZtjxgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107deaa50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('./data/pydata-book/ch08/Haiti.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Serial</th>\n",
       "      <th>INCIDENT TITLE</th>\n",
       "      <th>INCIDENT DATE</th>\n",
       "      <th>LOCATION</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "      <th>CATEGORY</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>APPROVED</th>\n",
       "      <th>VERIFIED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4052</td>\n",
       "      <td>* URGENT * Type O blood donations needed in #J...</td>\n",
       "      <td>05/07/2010 17:26</td>\n",
       "      <td>Jacmel, Haiti</td>\n",
       "      <td>Birthing Clinic in Jacmel #Haiti urgently need...</td>\n",
       "      <td>1. Urgences | Emergency, 3. Public Health,</td>\n",
       "      <td>18.233333</td>\n",
       "      <td>-72.533333</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4051</td>\n",
       "      <td>Food-Aid sent to Fondwa, Haiti</td>\n",
       "      <td>28/06/2010 23:06</td>\n",
       "      <td>fondwa</td>\n",
       "      <td>Please help food-aid.org deliver more food to ...</td>\n",
       "      <td>1. Urgences | Emergency, 2. Urgences logistiqu...</td>\n",
       "      <td>50.226029</td>\n",
       "      <td>5.729886</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4050</td>\n",
       "      <td>how haiti is right now and how it was during t...</td>\n",
       "      <td>24/06/2010 16:21</td>\n",
       "      <td>centrie</td>\n",
       "      <td>i feel so bad for you i know i am supposed to ...</td>\n",
       "      <td>2. Urgences logistiques | Vital Lines, 8. Autr...</td>\n",
       "      <td>22.278381</td>\n",
       "      <td>114.174287</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4049</td>\n",
       "      <td>Lost person</td>\n",
       "      <td>20/06/2010 21:59</td>\n",
       "      <td>Genoca</td>\n",
       "      <td>We are family members of Juan Antonio Zuniga O...</td>\n",
       "      <td>1. Urgences | Emergency,</td>\n",
       "      <td>44.407062</td>\n",
       "      <td>8.933989</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4042</td>\n",
       "      <td>Citi Soleil school</td>\n",
       "      <td>18/05/2010 16:26</td>\n",
       "      <td>Citi Soleil, Haiti</td>\n",
       "      <td>We are working with Haitian (NGO) -The Christi...</td>\n",
       "      <td>1. Urgences | Emergency,</td>\n",
       "      <td>18.571084</td>\n",
       "      <td>-72.334671</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Serial                                     INCIDENT TITLE  \\\n",
       "0    4052  * URGENT * Type O blood donations needed in #J...   \n",
       "1    4051                     Food-Aid sent to Fondwa, Haiti   \n",
       "2    4050  how haiti is right now and how it was during t...   \n",
       "3    4049                                        Lost person   \n",
       "4    4042                                 Citi Soleil school   \n",
       "\n",
       "      INCIDENT DATE            LOCATION  \\\n",
       "0  05/07/2010 17:26       Jacmel, Haiti   \n",
       "1  28/06/2010 23:06              fondwa   \n",
       "2  24/06/2010 16:21             centrie   \n",
       "3  20/06/2010 21:59              Genoca   \n",
       "4  18/05/2010 16:26  Citi Soleil, Haiti   \n",
       "\n",
       "                                         DESCRIPTION  \\\n",
       "0  Birthing Clinic in Jacmel #Haiti urgently need...   \n",
       "1  Please help food-aid.org deliver more food to ...   \n",
       "2  i feel so bad for you i know i am supposed to ...   \n",
       "3  We are family members of Juan Antonio Zuniga O...   \n",
       "4  We are working with Haitian (NGO) -The Christi...   \n",
       "\n",
       "                                            CATEGORY   LATITUDE   LONGITUDE  \\\n",
       "0        1. Urgences | Emergency, 3. Public Health,   18.233333  -72.533333   \n",
       "1  1. Urgences | Emergency, 2. Urgences logistiqu...  50.226029    5.729886   \n",
       "2  2. Urgences logistiques | Vital Lines, 8. Autr...  22.278381  114.174287   \n",
       "3                          1. Urgences | Emergency,   44.407062    8.933989   \n",
       "4                          1. Urgences | Emergency,   18.571084  -72.334671   \n",
       "\n",
       "  APPROVED VERIFIED  \n",
       "0      YES       NO  \n",
       "1       NO       NO  \n",
       "2       NO       NO  \n",
       "3       NO       NO  \n",
       "4      YES       NO  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3593 entries, 0 to 3592\n",
      "Data columns (total 10 columns):\n",
      "Serial            3593 non-null int64\n",
      "INCIDENT TITLE    3593 non-null object\n",
      "INCIDENT DATE     3593 non-null object\n",
      "LOCATION          3592 non-null object\n",
      "DESCRIPTION       3593 non-null object\n",
      "CATEGORY          3587 non-null object\n",
      "LATITUDE          3593 non-null float64\n",
      "LONGITUDE         3593 non-null float64\n",
      "APPROVED          3593 non-null object\n",
      "VERIFIED          3593 non-null object\n",
      "dtypes: float64(2), int64(1), object(7)\n",
      "memory usage: 280.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INCIDENT DATE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>05/07/2010 17:26</td>\n",
       "      <td>18.233333</td>\n",
       "      <td>-72.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28/06/2010 23:06</td>\n",
       "      <td>50.226029</td>\n",
       "      <td>5.729886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24/06/2010 16:21</td>\n",
       "      <td>22.278381</td>\n",
       "      <td>114.174287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20/06/2010 21:59</td>\n",
       "      <td>44.407062</td>\n",
       "      <td>8.933989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18/05/2010 16:26</td>\n",
       "      <td>18.571084</td>\n",
       "      <td>-72.334671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>26/04/2010 13:14</td>\n",
       "      <td>18.593707</td>\n",
       "      <td>-72.310079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>26/04/2010 14:19</td>\n",
       "      <td>18.482800</td>\n",
       "      <td>-73.638800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26/04/2010 14:27</td>\n",
       "      <td>18.415000</td>\n",
       "      <td>-73.195000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15/03/2010 10:58</td>\n",
       "      <td>18.517443</td>\n",
       "      <td>-72.236841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15/03/2010 11:00</td>\n",
       "      <td>18.547790</td>\n",
       "      <td>-72.410010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      INCIDENT DATE   LATITUDE   LONGITUDE\n",
       "0  05/07/2010 17:26  18.233333  -72.533333\n",
       "1  28/06/2010 23:06  50.226029    5.729886\n",
       "2  24/06/2010 16:21  22.278381  114.174287\n",
       "3  20/06/2010 21:59  44.407062    8.933989\n",
       "4  18/05/2010 16:26  18.571084  -72.334671\n",
       "5  26/04/2010 13:14  18.593707  -72.310079\n",
       "6  26/04/2010 14:19  18.482800  -73.638800\n",
       "7  26/04/2010 14:27  18.415000  -73.195000\n",
       "8  15/03/2010 10:58  18.517443  -72.236841\n",
       "9  15/03/2010 11:00  18.547790  -72.410010"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['INCIDENT DATE','LATITUDE','LONGITUDE']][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Serial</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3593.000000</td>\n",
       "      <td>3593.000000</td>\n",
       "      <td>3593.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2080.277484</td>\n",
       "      <td>18.611495</td>\n",
       "      <td>-72.322680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1171.100360</td>\n",
       "      <td>0.738572</td>\n",
       "      <td>3.650776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>18.041313</td>\n",
       "      <td>-74.452757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1074.000000</td>\n",
       "      <td>18.524070</td>\n",
       "      <td>-72.417500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2163.000000</td>\n",
       "      <td>18.539269</td>\n",
       "      <td>-72.335000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3088.000000</td>\n",
       "      <td>18.561820</td>\n",
       "      <td>-72.293570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4052.000000</td>\n",
       "      <td>50.226029</td>\n",
       "      <td>114.174287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Serial     LATITUDE    LONGITUDE\n",
       "count  3593.000000  3593.000000  3593.000000\n",
       "mean   2080.277484    18.611495   -72.322680\n",
       "std    1171.100360     0.738572     3.650776\n",
       "min       4.000000    18.041313   -74.452757\n",
       "25%    1074.000000    18.524070   -72.417500\n",
       "50%    2163.000000    18.539269   -72.335000\n",
       "75%    3088.000000    18.561820   -72.293570\n",
       "max    4052.000000    50.226029   114.174287"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = data[(data.LATITUDE>18)&(data.LATITUDE<20)&(data.LONGITUDE>-75)&(data.LONGITUDE<-70)&(data.CATEGORY.notnull())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Serial</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3569.000000</td>\n",
       "      <td>3569.000000</td>\n",
       "      <td>3569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2081.498459</td>\n",
       "      <td>18.592503</td>\n",
       "      <td>-72.424994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1170.311824</td>\n",
       "      <td>0.273695</td>\n",
       "      <td>0.291018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>18.041313</td>\n",
       "      <td>-74.452757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1074.000000</td>\n",
       "      <td>18.524200</td>\n",
       "      <td>-72.417498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2166.000000</td>\n",
       "      <td>18.539269</td>\n",
       "      <td>-72.335000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3089.000000</td>\n",
       "      <td>18.561800</td>\n",
       "      <td>-72.293939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4052.000000</td>\n",
       "      <td>19.940630</td>\n",
       "      <td>-71.099489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Serial     LATITUDE    LONGITUDE\n",
       "count  3569.000000  3569.000000  3569.000000\n",
       "mean   2081.498459    18.592503   -72.424994\n",
       "std    1170.311824     0.273695     0.291018\n",
       "min       4.000000    18.041313   -74.452757\n",
       "25%    1074.000000    18.524200   -72.417498\n",
       "50%    2166.000000    18.539269   -72.335000\n",
       "75%    3089.000000    18.561800   -72.293939\n",
       "max    4052.000000    19.940630   -71.099489"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def to_cat_list(catstr):\n",
    "    stripped = (x.strip() for x in catstr.split(','))\n",
    "    return [x for x in stripped if x]\n",
    "def get_all_categories(cat_series):\n",
    "    cat_sets = (set(to_cat_list(x)) for x in cat_series)\n",
    "    return sorted(set.union(*cat_sets))\n",
    "def get_english(cat):\n",
    "    code,names= cat.split(\".\")\n",
    "    if '|' in names:\n",
    "        names = names.split(' | ')[1]\n",
    "    return code,names.strip()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2', 'Vital Lines')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_english('2. Urgences logistiques | Vital Lines')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_cats = get_all_categories(data.CATEGORY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "english_mapping = dict(get_english(x) for x in all_cats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Vital Lines'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "english_mapping['2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Earthquake and aftershocks'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "english_mapping['6c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_code(seq):\n",
    "    return [x.split('.')[0] for x in seq if x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_codes = get_code(all_cats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "code_index = pd.Index(np.unique(all_codes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dummy_frame = DataFrame(np.zeros((len(data),len(code_index))),index =data.index,columns = code_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>1a</th>\n",
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       "      <th>8</th>\n",
       "      <th>8a</th>\n",
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       "      <th>8d</th>\n",
       "      <th>8e</th>\n",
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       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
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       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for row,cat in zip(data.index,data.CATEGORY):\n",
    "    codes = get_code(to_cat_list(cat))\n",
    "    dummy_frame.ix[row,codes]=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=data.join(dummy_frame.add_prefix('category_'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4040</td>\n",
       "      <td>Contaminated water in Baraderes.</td>\n",
       "      <td>26/04/2010 14:19</td>\n",
       "      <td>Marc near Baraderes</td>\n",
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       "      <td>4039</td>\n",
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       "      <td>26/04/2010 14:27</td>\n",
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       "   Serial                                     INCIDENT TITLE  \\\n",
       "0    4052  * URGENT * Type O blood donations needed in #J...   \n",
       "4    4042                                 Citi Soleil school   \n",
       "5    4041                           Radio Commerce in Sarthe   \n",
       "6    4040                   Contaminated water in Baraderes.   \n",
       "7    4039     Violence at &quot;arcahaie bas Saint-Ard&quot;   \n",
       "\n",
       "      INCIDENT DATE                                           LOCATION  \\\n",
       "0  05/07/2010 17:26                                      Jacmel, Haiti   \n",
       "4  18/05/2010 16:26                                 Citi Soleil, Haiti   \n",
       "5  26/04/2010 13:14                     Radio Commerce Shelter, Sarthe   \n",
       "6  26/04/2010 14:19                                Marc near Baraderes   \n",
       "7  26/04/2010 14:27  unable to find &quot;arcahaie bas Saint-Ard&qu...   \n",
       "\n",
       "                                         DESCRIPTION  \\\n",
       "0  Birthing Clinic in Jacmel #Haiti urgently need...   \n",
       "4  We are working with Haitian (NGO) -The Christi...   \n",
       "5  i'm Louinel from Sarthe. I'd to know what can ...   \n",
       "6  How do we treat water in areas without Pipe?\\t...   \n",
       "7  Goodnight at (arcahaie bas Saint-Ard) 2 young ...   \n",
       "\n",
       "                                            CATEGORY   LATITUDE  LONGITUDE  \\\n",
       "0        1. Urgences | Emergency, 3. Public Health,   18.233333 -72.533333   \n",
       "4                          1. Urgences | Emergency,   18.571084 -72.334671   \n",
       "5                     5e. Communication lines down,   18.593707 -72.310079   \n",
       "6  4. Menaces | Security Threats, 4e. Assainissem...  18.482800 -73.638800   \n",
       "7                    4. Menaces | Security Threats,   18.415000 -73.195000   \n",
       "\n",
       "  APPROVED VERIFIED     ...       category_7c  category_7d  category_7g  \\\n",
       "0      YES       NO     ...               0.0          0.0          0.0   \n",
       "4      YES       NO     ...               0.0          0.0          0.0   \n",
       "5      YES       NO     ...               0.0          0.0          0.0   \n",
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       "\n",
       "   category_7h  category_8  category_8a  category_8c  category_8d  \\\n",
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       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3569 entries, 0 to 3592\n",
      "Data columns (total 55 columns):\n",
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      "INCIDENT TITLE    3569 non-null object\n",
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      "LOCATION          3568 non-null object\n",
      "DESCRIPTION       3569 non-null object\n",
      "CATEGORY          3569 non-null object\n",
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      "category_8d       3569 non-null float64\n",
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      "category_8f       3569 non-null float64\n",
      "dtypes: float64(47), int64(1), object(7)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.005849</td>\n",
       "      <td>0.014215</td>\n",
       "      <td>0.088193</td>\n",
       "      <td>-0.128617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.006838</td>\n",
       "      <td>-0.008505</td>\n",
       "      <td>0.215321</td>\n",
       "      <td>0.038466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000681</td>\n",
       "      <td>-0.003565</td>\n",
       "      <td>0.125317</td>\n",
       "      <td>0.055060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.005772</td>\n",
       "      <td>-0.002861</td>\n",
       "      <td>-0.212805</td>\n",
       "      <td>-0.074108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000338</td>\n",
       "      <td>0.004289</td>\n",
       "      <td>-0.266946</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.006745</td>\n",
       "      <td>0.004980</td>\n",
       "      <td>-0.127155</td>\n",
       "      <td>0.074108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.003021</td>\n",
       "      <td>0.001418</td>\n",
       "      <td>-0.030110</td>\n",
       "      <td>0.117783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.001006</td>\n",
       "      <td>0.007062</td>\n",
       "      <td>0.034338</td>\n",
       "      <td>0.076373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.003683</td>\n",
       "      <td>0.005614</td>\n",
       "      <td>-0.034338</td>\n",
       "      <td>0.028988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.002003</td>\n",
       "      <td>0.008362</td>\n",
       "      <td>0.013015</td>\n",
       "      <td>-0.028988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.001999</td>\n",
       "      <td>0.007605</td>\n",
       "      <td>0.113944</td>\n",
       "      <td>-0.092373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.005643</td>\n",
       "      <td>0.008230</td>\n",
       "      <td>0.048790</td>\n",
       "      <td>-0.101783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.000331</td>\n",
       "      <td>0.000683</td>\n",
       "      <td>0.018149</td>\n",
       "      <td>-0.018019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.005281</td>\n",
       "      <td>0.001364</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.001973</td>\n",
       "      <td>0.010848</td>\n",
       "      <td>0.031861</td>\n",
       "      <td>-0.018019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.001313</td>\n",
       "      <td>0.009396</td>\n",
       "      <td>0.010399</td>\n",
       "      <td>0.053110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.006866</td>\n",
       "      <td>0.010631</td>\n",
       "      <td>0.043852</td>\n",
       "      <td>-0.017392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.008555</td>\n",
       "      <td>0.109313</td>\n",
       "      <td>-0.035718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.006160</td>\n",
       "      <td>0.007183</td>\n",
       "      <td>0.040585</td>\n",
       "      <td>0.018019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.000323</td>\n",
       "      <td>0.007131</td>\n",
       "      <td>-0.002845</td>\n",
       "      <td>-0.018019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.002259</td>\n",
       "      <td>0.012837</td>\n",
       "      <td>-0.011461</td>\n",
       "      <td>-0.056089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.003219</td>\n",
       "      <td>0.015190</td>\n",
       "      <td>0.017143</td>\n",
       "      <td>-0.039221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.005128</td>\n",
       "      <td>0.009378</td>\n",
       "      <td>0.063121</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.003192</td>\n",
       "      <td>0.008057</td>\n",
       "      <td>0.044220</td>\n",
       "      <td>-0.020203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.006353</td>\n",
       "      <td>0.006767</td>\n",
       "      <td>-0.023167</td>\n",
       "      <td>-0.041673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.002214</td>\n",
       "      <td>0.017624</td>\n",
       "      <td>0.023167</td>\n",
       "      <td>-0.065958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.007241</td>\n",
       "      <td>0.018502</td>\n",
       "      <td>0.101536</td>\n",
       "      <td>-0.070618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.012469</td>\n",
       "      <td>0.015841</td>\n",
       "      <td>0.060219</td>\n",
       "      <td>-0.050010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.005253</td>\n",
       "      <td>-0.008769</td>\n",
       "      <td>0.006473</td>\n",
       "      <td>-0.025975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.012251</td>\n",
       "      <td>0.005271</td>\n",
       "      <td>0.117544</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>0.003896</td>\n",
       "      <td>0.011403</td>\n",
       "      <td>-0.028988</td>\n",
       "      <td>0.017392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>0.006645</td>\n",
       "      <td>0.003745</td>\n",
       "      <td>-0.054394</td>\n",
       "      <td>-0.017392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>0.007697</td>\n",
       "      <td>0.018760</td>\n",
       "      <td>-0.293913</td>\n",
       "      <td>0.017392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>0.003280</td>\n",
       "      <td>0.017454</td>\n",
       "      <td>-0.051293</td>\n",
       "      <td>0.017094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>0.002726</td>\n",
       "      <td>0.031150</td>\n",
       "      <td>-0.171850</td>\n",
       "      <td>0.049597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>0.006511</td>\n",
       "      <td>0.007272</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>-0.016261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>0.007543</td>\n",
       "      <td>0.006760</td>\n",
       "      <td>-0.043485</td>\n",
       "      <td>-0.050431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>0.005887</td>\n",
       "      <td>0.019711</td>\n",
       "      <td>0.043485</td>\n",
       "      <td>-0.017392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>0.009031</td>\n",
       "      <td>0.006286</td>\n",
       "      <td>0.252496</td>\n",
       "      <td>-0.017700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>0.008950</td>\n",
       "      <td>0.015177</td>\n",
       "      <td>0.297960</td>\n",
       "      <td>-0.036368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>0.005227</td>\n",
       "      <td>0.004106</td>\n",
       "      <td>0.299877</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>0.010374</td>\n",
       "      <td>-0.006460</td>\n",
       "      <td>0.201084</td>\n",
       "      <td>-0.018692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>0.004633</td>\n",
       "      <td>0.006460</td>\n",
       "      <td>0.112399</td>\n",
       "      <td>-0.038466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>0.022849</td>\n",
       "      <td>0.006128</td>\n",
       "      <td>0.156521</td>\n",
       "      <td>-0.019803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>0.001004</td>\n",
       "      <td>0.004064</td>\n",
       "      <td>0.127833</td>\n",
       "      <td>-0.020203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>0.006498</td>\n",
       "      <td>-0.000072</td>\n",
       "      <td>0.120003</td>\n",
       "      <td>-0.041673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>0.009916</td>\n",
       "      <td>-0.008219</td>\n",
       "      <td>0.066477</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>-0.003955</td>\n",
       "      <td>0.000146</td>\n",
       "      <td>0.016461</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>0.008257</td>\n",
       "      <td>0.003062</td>\n",
       "      <td>0.004073</td>\n",
       "      <td>-0.065958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>0.011458</td>\n",
       "      <td>0.004431</td>\n",
       "      <td>0.006079</td>\n",
       "      <td>0.022473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>0.006863</td>\n",
       "      <td>-0.007055</td>\n",
       "      <td>-0.047579</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>0.008620</td>\n",
       "      <td>0.006693</td>\n",
       "      <td>-0.165514</td>\n",
       "      <td>0.043485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>0.015948</td>\n",
       "      <td>-0.001306</td>\n",
       "      <td>-0.284354</td>\n",
       "      <td>0.021053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>0.007044</td>\n",
       "      <td>0.004780</td>\n",
       "      <td>-0.657254</td>\n",
       "      <td>0.020619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>0.021327</td>\n",
       "      <td>0.018115</td>\n",
       "      <td>0.109199</td>\n",
       "      <td>0.097164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>-0.007904</td>\n",
       "      <td>0.045361</td>\n",
       "      <td>-0.396881</td>\n",
       "      <td>0.105361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>-0.021979</td>\n",
       "      <td>0.066753</td>\n",
       "      <td>-2.277267</td>\n",
       "      <td>0.139762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>0.002340</td>\n",
       "      <td>0.010286</td>\n",
       "      <td>0.606136</td>\n",
       "      <td>0.160343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>0.008419</td>\n",
       "      <td>0.037461</td>\n",
       "      <td>-0.200671</td>\n",
       "      <td>0.127339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>0.008894</td>\n",
       "      <td>0.012202</td>\n",
       "      <td>-0.405465</td>\n",
       "      <td>0.042560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>202 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          cpi        m1  tbilrate     unemp\n",
       "1    0.005849  0.014215  0.088193 -0.128617\n",
       "2    0.006838 -0.008505  0.215321  0.038466\n",
       "3    0.000681 -0.003565  0.125317  0.055060\n",
       "4    0.005772 -0.002861 -0.212805 -0.074108\n",
       "5    0.000338  0.004289 -0.266946  0.000000\n",
       "6    0.006745  0.004980 -0.127155  0.074108\n",
       "7    0.003021  0.001418 -0.030110  0.117783\n",
       "8   -0.001006  0.007062  0.034338  0.076373\n",
       "9    0.003683  0.005614 -0.034338  0.028988\n",
       "10   0.002003  0.008362  0.013015 -0.028988\n",
       "11   0.001999  0.007605  0.113944 -0.092373\n",
       "12   0.005643  0.008230  0.048790 -0.101783\n",
       "13   0.000331  0.000683  0.018149 -0.018019\n",
       "14   0.005281  0.001364  0.000000  0.018019\n",
       "15   0.001973  0.010848  0.031861 -0.018019\n",
       "16   0.001313  0.009396  0.010399  0.053110\n",
       "17   0.006866  0.010631  0.043852 -0.017392\n",
       "18   0.001953  0.008555  0.109313 -0.035718\n",
       "19   0.006160  0.007183  0.040585  0.018019\n",
       "20   0.000323  0.007131 -0.002845 -0.018019\n",
       "21   0.002259  0.012837 -0.011461 -0.056089\n",
       "22   0.003219  0.015190  0.017143 -0.039221\n",
       "23   0.005128  0.009378  0.063121  0.000000\n",
       "24   0.003192  0.008057  0.044220 -0.020203\n",
       "25   0.006353  0.006767 -0.023167 -0.041673\n",
       "26   0.002214  0.017624  0.023167 -0.065958\n",
       "27   0.007241  0.018502  0.101536 -0.070618\n",
       "28   0.012469  0.015841  0.060219 -0.050010\n",
       "29   0.005253 -0.008769  0.006473 -0.025975\n",
       "30   0.012251  0.005271  0.117544  0.000000\n",
       "..        ...       ...       ...       ...\n",
       "173  0.003896  0.011403 -0.028988  0.017392\n",
       "174  0.006645  0.003745 -0.054394 -0.017392\n",
       "175  0.007697  0.018760 -0.293913  0.017392\n",
       "176  0.003280  0.017454 -0.051293  0.017094\n",
       "177  0.002726  0.031150 -0.171850  0.049597\n",
       "178  0.006511  0.007272 -0.021053 -0.016261\n",
       "179  0.007543  0.006760 -0.043485 -0.050431\n",
       "180  0.005887  0.019711  0.043485 -0.017392\n",
       "181  0.009031  0.006286  0.252496 -0.017700\n",
       "182  0.008950  0.015177  0.297960 -0.036368\n",
       "183  0.005227  0.004106  0.299877  0.000000\n",
       "184  0.010374 -0.006460  0.201084 -0.018692\n",
       "185  0.004633  0.006460  0.112399 -0.038466\n",
       "186  0.022849  0.006128  0.156521 -0.019803\n",
       "187  0.001004  0.004064  0.127833 -0.020203\n",
       "188  0.006498 -0.000072  0.120003 -0.041673\n",
       "189  0.009916 -0.008219  0.066477  0.000000\n",
       "190 -0.003955  0.000146  0.016461  0.000000\n",
       "191  0.008257  0.003062  0.004073 -0.065958\n",
       "192  0.011458  0.004431  0.006079  0.022473\n",
       "193  0.006863 -0.007055 -0.047579  0.000000\n",
       "194  0.008620  0.006693 -0.165514  0.043485\n",
       "195  0.015948 -0.001306 -0.284354  0.021053\n",
       "196  0.007044  0.004780 -0.657254  0.020619\n",
       "197  0.021327  0.018115  0.109199  0.097164\n",
       "198 -0.007904  0.045361 -0.396881  0.105361\n",
       "199 -0.021979  0.066753 -2.277267  0.139762\n",
       "200  0.002340  0.010286  0.606136  0.160343\n",
       "201  0.008419  0.037461 -0.200671  0.127339\n",
       "202  0.008894  0.012202 -0.405465  0.042560\n",
       "\n",
       "[202 rows x 4 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named axes_grid1",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-5e6824321d57>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mmpl_toolkits\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasemap\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mBasemap\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/mpl_toolkits/basemap/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransforms\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mBbox\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpyproj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mmpl_toolkits\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes_grid1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmake_axes_locatable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     34\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mimread\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: No module named axes_grid1"
     ]
    }
   ],
   "source": [
    "from mpl_toolkits.basemap import Basemap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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